This invention relates to a computer software-based design system for controlling a material consolidation process and for modeling the consolidation process and resulting materials through the use of finite element techniques.
It has long been recognized that the presence of porosity in material structures has a deleterious effect on the physical and mechanical integrity of the structures. Thus, the removal of porosity is viewed as an important means for achieving densification and enhancing the properties of the material in question.
Over the past twenty years there has been a considerable emphasis in the use of material consolidation processing techniques in industrial applications. The material consolidation process increases the relative density of the material/structure by reducing the fraction of macrostructural voids in the material. Typically, the materials being consolidated can include any powder compact or composite material such as, for example, metal castings or forgings as ceramic extrusions, plastics or glass. The voids or porosity contained in these material structures typically result from previous processing steps. With society's heightened awareness that it has precious natural resources and the need to economically utilize often expensive raw materials, it has become more desirable to reduce the amount of material required to meet manufacturing needs. Thus, there is a very real need to produce components having near-net shape so that the amount of machining or secondary processing required to complete production of the component may be minimized. This results in significant savings in cost, time, manpower, energy and other natural resources, and will also reduce the amount of waste material generated.
Consolidation is accomplished by exposing the material structure to elevated temperature and pressure for an extended period of time. One such consolidation process is commonly known as hot isostatic pressing ("HIP"). The process is used to consolidate, i.e., densify a variety of material structures. In powder metallurgical applications, for example, the powdered metal is poured into a container. The container is then outgassed, sealed, and transferred into the HIP unit. The container is then subjected to high temperature and pressure for a period of time. During the HIP cycle the individual powder grains are consolidated and the voids or porosity interposed between adjacent grains are eliminated. Typically, the resulting structure is fully densified, more structurally homogenous and very close to its final shape. HIPing may also be used to consolidate any other solid structures such as castings and forgings.
Not surprisingly, there are inherent drawbacks associated with consolidation processes such as HIP. The most significant drawback is simply the high cost of performing these operations.
In the conventional process control setting, a preset control algorithm forms the basis for process actuation of a time-temperature-pressure schedule aimed at full densification of material structures such as powder compacts. Sensing usually consists of a network of thermocouples and pressure gauges located within the consolidation chamber. The process parameters are continuously monitored for deviations from the prescribed set point values for temperature and pressure, and any deviations therefrom are corrected through actuation. Thus, the feedback control is based exclusively on pressure and temperature. Importantly, however, this type of process control does not monitor the material response, but only the consolidation environment. Practically speaking, the conventional approach to process control only addresses part of the problem, i.e. process environment, and does not address the more difficult issue of material response.
A more powerful approach to reducing the cost of material consolidation processing is through excerising control of the material state. This approach involves the monitoring and adjusting consolidation process parameters--while the consolidation process is actually being conducted. This, of course, permits the user to ascertain how the consolidation process is progressing. Moreover, to the extent that material behavior deviates from what was initially predicted during simulation and design optimization, the process control functions permit the appropriate process parameters to reverse the deviation.
The missing link in the process control chain is the means for directly monitoring and comparing the actual material response to the targeted response at each stage of the consolidation process. To accomplish that requires two things, namely, an in-situ sensing device which can monitor material response and provide data, and second an external, open control loop for receiving and processing the data from the in-situ sensor. Specifically, the external control processes the data in order to determine material density, grain size and other micromechanical or microstructural properties which dictate the final product properties.
In one type of a more advanced process control environment, referred to herein as Intelligent Processing of Materials (IPM), density and microstructure are directly controlled through the combination of in-situ sensing coupled with an on-line intelligent, user interactive, process controller which interfaces with a sophisticated simulation system that in turn processes and integrates the sensor data. The simulation system makes comparisons between the actual and expected process trajectories, identifies and quantifies deviations and then generates process schedule adjustments to correct for deviations between actual and expected material response. The process control operator then evaluates the recommendations, in light of the system constraints and processing goals and institutes the appropriate process control response. IPM is neither a statistical process control nor an increased use of existing sensors, such as temperature, pressure and flow rate sensors. Further, IPM is not a fixed, computer-controlled process trajectory in process variable space. Moreover, its is not research into artificial intelligence, although it may draw upon artificial intelligence to assist in processing and integrating the sensor data, and formulating corrective action.
In an IPM environment, the in-situ sensing is coupled to an extrinsic control loop incorporated as part of the control system. The sensing device provides continuous material response capability and thus, the state of the material may be identified at any time during the consolidation process. Comparisons of the actual material response, and/or consolidation path, are made against those predicted by the simulation system. The simulation system operates in such a manner that it can integrate the sensor data using the appropriate constitutive equations that govern material densification, grain growth and other microstructural properties, linking those material properties to the macroscopic material behavior exhibited by the specimens being consolidated.
In the IPM environment, a critical consideration is the effectiveness of the simulation system for purposes of providing initial material consolidation schedules and component designs, and for monitoring, comparing and correcting actual material response to insure attaining the desired properties in the finished products. Ideally, the optimum simulation system would be totally accurate in making predictions, would be applicable to all materials in whatever forms, and would be quick and easy to operate. At present, such a system does not exist. Although simulation and modeling systems do exist, as explained below, there are a number of limitations inherent to most of those systems.
With process simulation, a proposed component design and selected material are subjected to a consolidation schedule (time, temperature and pressure). By then simulating the consolidation process, subject to these schedule parameters, the material behavior may be modeled in order to predict how that component would actually respond if the test were physically carried out. The primary purpose of simulation is to minimize the need for experimental testing.
A number of simulation systems have been developed over the years, however, their effectiveness is limited. These limitations include simulation systems that are restricted only to one or two dimensional analyses thereby precluding their applicability to complex geometries, or systems based on empirical data obtained through ad hoc experimental testing on a single material or class of materials thereby limiting these system's applicability to single material simulation. A further limitation in many models, is that the system does not model all of the densification mechanisms which affect the accuracy of the final solution. Additionally, many of the simulation systems do not simulate material behavior through the broad range of relative densities typically encountered in the materials being consolidated. Thus, empirical models derived from testing of Stage II material structures (relative density &gt;0.90) may not accurately predict the material response of Stage I material structures (relative density &lt;0.90).
An example of some of the above cited limitations is demonstrated by the HIP process simulation program developed by Abouaf et al.
Abouaf et al. is illustrative of the ad hoc modeling approach because the constitutive equations which form the basis of the modeling solution are derived from experimental testing of a single material: an Astroloy powder. Thus, the constitutive equations are limited to Astroloy. Additionally, Abouaf is limited to two-dimensional FEM analysis of the axisymmetric components. Although Abouaf et al. do model mechanisms for plasticity and creep, modeling of densification by diffusion is absent.
Another example of a simulation system is the DEFORM.TM. System by Battelle, Columbus, Ohio (DEFORM is an achronym for Design Environment for Forming). That modeling system is based upon rigid-plastic formulation which disregards elastic responses and only models the plasticity densification mechanism. Because of these various limitations, the simulation results may require further refining through experimental testing before the proposed process can be introduced into production.
In formulating these simulation systems, a system designer must also identify and implement numerous relationships which describe the physical and mechanical behavior of the material. Not surprisingly, these relationships can be quite complicated to define depending on the underlying assumptions. Successfully implementing them into a software modeling program thereby represents a considerable accomplishment. Often times simplifying assumptions may be made in developing the constitutive relationship in order to make the solutions more manageable. As a consequence, however, these assumptions may represent significant limitations in the constitutive relationships thereby limiting the scope of their application. Moreover, those fundamental physical and mechanical limitations are part of the chosen solution and they cannot be "designed out" when adapting the constitutive relationships into a modeling or control system. Thus, while the underlying assumptions ma simplify the development of the constitutive relationship, the resulting modeling or control system cannot overcome the limitations inherent in the solution.
Another problem associated with the development of software programs in general, and especially those based upon FEM, is that such programs are particularly susceptible to generating solutions that are physically impossible to implement. On the other hand, a simulation output can be numerically stable, but physically unstable due, for example, to nonlinear distortions which cause buckling, collapse, and the like. It is important that the software program and subroutines upon which the simulation system is based, can consistently generate stable solutions which do not influence or otherwise interfere with the prediction of physical instabilities.
Additionally, it is highly desirable that the simulation system provide the user with versatile output visualization capabilities. The need for visual representations of the model are apparent, given the limited utility of a tabulated data output. Many of the programs presently available do incorporate graphics packages for limited output visualization. For example, the previously discussed DEFORM.TM. program and the HIPNAS.TM. program (a product of Kobe Steel) as well as Abouaf et al. have visualization capabilities. In addition to output visualization, however, it is desirable to have a highly cognitive user-interface which allows for output review at any time during the simulation, and further permits the user to optimize the consolidation process by modifying component design, material selection and properties as well as consolidation parameters, (time, temperature and pressure).
It is useful to provide process simulation functions, which has both simulation and modeling capabilities, and also permits user feedback for purposes of design/process optimization.
Based on the foregoing, it is clear that there exists a need for IPM, and that such a control environment is especially important as materials technology advances and newer high technology materials are developed. Therefore, even though modeling systems and consolidation process control both represent cost savings to the end user, there still exists a need for a diversified material consolidation control system having functional capabilities for simulation, component design and the process schedule optimization and interactive or active process control in production setting. An ideal system would not only allow the user to simulate the response of a material subjected to a prescribed consolidation environment, but would also allow the user to optimize material selection, component design and consolidation schedule as well. The optimized processing parameters would constitute input for the intelligent controller which would then initiate the consolidation cycle. Material sensing equipment would feed data back to the simulation system which would in turn integrate the data using the same modeling information used to arrive at the initial set of process parameters. The simulation would then make comparisons of the actual to the expected process trajectory and would generate recommendations to the system operator as to what corrective actions may be taken.